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petigura/5_fits-to-simulated-data.pdf.ipynb

Created June 10, 2020 00:02
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5_fits-to-simulated-data.pdf.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Imports and defining some functions "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline\n",
"#%load_ext line_profiler\n",
"import exoplanet as xo\n",
"import pandas as pd\n",
"import pymc3 as pm\n",
"import theano.tensor as tt\n",
"import seaborn as sns\n",
"rcParams['figure.dpi'] = 160\n",
"def _tfold(t, per, tc):\n",
" return np.mod(((t - tc) + 0.5 * per),per) - 0.5 * per\n",
"\n",
"texp_lc = 0.02\n",
"texp_sc = texp_lc/30"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"rcParams['figure.dpi'] = 160 # execute twice"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generate Long and Short Cadence Light Curve"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/petigura/anaconda2/envs/py37/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" rval = inputs[0].__getitem__(inputs[1:])\n"
]
}
],
"source": [
"np.random.seed(42)\n",
"per = 11.512312\n",
"b=0.9\n",
"r=0.0244\n",
"\n",
"truth = dict(per=11.512312, tc=5.124124,b=b, r=r, ecc=0.0, omega=90)\n",
"u = np.array([0.4451, 0.2297])\n",
"\n",
"yerr_lc = 2e-4 / 1.48\n",
"yerr_sc = yerr_lc*sqrt(texp_lc/texp_sc)\n",
"\n",
"t_sc = np.arange(0, 1400, texp_sc)\n",
"orbit0 = xo.orbits.KeplerianOrbit(\n",
" period=truth['per'], \n",
" t0=truth['tc'], \n",
" b=truth['b'], \n",
" ecc=truth['ecc'],\n",
" omega=truth['omega'],\n",
" rho_star = 1 # grams per / cc\n",
")\n",
"\n",
"#\n",
"model_sc = xo.LimbDarkLightCurve(u).get_light_curve(orbit=orbit0, r=truth['r'], t=t_sc,texp=0.02/30).eval()\n",
"data_sc = (model_sc + yerr_sc * np.random.randn(*model_sc.shape)).reshape(-1) # need this for single planet fit\n",
"tfold_sc = _tfold(t_sc, truth['per'],truth['tc'])\n",
"data_lc = data_sc.reshape(-1,30).mean(axis=1)\n",
"model_lc = model_sc.reshape(-1,30).mean(axis=1)\n",
"t_lc = t_sc.reshape(-1,30).mean(axis=1)\n",
"\n",
"bcut = abs(tfold_sc) < 0.1\n",
"t_sc = t_sc[bcut]\n",
"model_sc = model_sc[bcut]\n",
"data_sc = data_sc[bcut]\n",
"tfold_sc = tfold_sc[bcut]\n",
"isort_sc = argsort(tfold_sc)\n",
"\n",
"tfold_lc = _tfold(t_lc, truth['per'],truth['tc'])\n",
"bcut = abs(tfold_lc) < 0.1\n",
"t_lc = t_lc[bcut]\n",
"model_lc = model_lc[bcut]\n",
"data_lc = data_lc[bcut]\n",
"tfold_lc = tfold_lc[bcut]\n",
"isort_lc = argsort(tfold_lc)\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/petigura/anaconda2/envs/py37/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" rval = inputs[0].__getitem__(inputs[1:])\n",
"optimizing logp for variables: [r]\n",
"0it [00:00, ?it/s]/Users/petigura/anaconda2/envs/py37/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" rval = inputs[0].__getitem__(inputs[1:])\n",
"13it [00:00, 37.76it/s, logp=8.610476e+03]/Users/petigura/anaconda2/envs/py37/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" rval = inputs[0].__getitem__(inputs[1:])\n",
"14it [00:00, 16.76it/s, logp=8.610476e+03]\n",
"message: Optimization terminated successfully.\n",
"logp: -191781.16857513803 -> 8610.475973939056\n",
"optimizing logp for variables: [logrho, r]\n",
"0it [00:00, ?it/s]/Users/petigura/anaconda2/envs/py37/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" rval = inputs[0].__getitem__(inputs[1:])\n",
"74it [00:01, 75.72it/s, logp=9.744769e+03]/Users/petigura/anaconda2/envs/py37/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" rval = inputs[0].__getitem__(inputs[1:])\n",
"86it [00:01, 76.37it/s, logp=9.744769e+03]/Users/petigura/anaconda2/envs/py37/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" rval = inputs[0].__getitem__(inputs[1:])\n",
"87it [00:01, 44.56it/s, logp=9.744769e+03]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'r_interval__': array(-1.38443035), 'logrho_interval__': array(0.21294521), 'r': array(0.02002984), 'logrho': array(1.06072084), 'rho': array(11.50060895), 'light_curve': array([0., 0., 0., ..., 0., 0., 0.])}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"message: Desired error not necessarily achieved due to precision loss.\n",
"logp: 8610.475973939056 -> 9744.76892263019\n"
]
},
{
"data": {
"text/plain": [
"Text(0.5, 0, 'Time Since Mid-Transit (days)')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"<Figure size 960x640 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 960x960 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Plot long cadence light curve\n",
"figure()\n",
"\n",
"with pm.Model() as xo_model_lc:\n",
" mean = 0\n",
" r = pm.Uniform(\"r\", lower=0.00, upper=0.1, testval=0.05)\n",
" b = 0 \n",
" logrho = pm.Uniform(\"logrho\", lower=-10, upper=10, testval=0)\n",
" rho = pm.Deterministic(\"rho\", 10**logrho)\n",
" orbit = xo.orbits.KeplerianOrbit(period=truth['per'], t0=truth['tc'], b=b, rho_star=rho)\n",
" light_curves = xo.LimbDarkLightCurve(u).get_light_curve(orbit=orbit, r=r, t=t_lc, texp=0.02, oversample=100,order=2) \n",
" light_curve = pm.math.sum(light_curves, axis=-1) + mean\n",
" pm.Deterministic(\"light_curve\", light_curve)\n",
" pm.Normal(\"obs\", mu=light_curve, sd=yerr_lc, observed=model_lc)\n",
" map_soln = xo.optimize(start=xo_model_lc.test_point,vars=[r])\n",
" map_soln = xo.optimize(start=map_soln)\n",
"\n",
"print(map_soln)\n",
" \n",
"fig = figure(figsize=(6,6))\n",
"gs = GridSpec(3, 1, figure=fig)\n",
"ax1 = fig.add_subplot(gs[0:-1])\n",
"# identical to ax1 = plt.subplot(gs.new_subplotspec((0, 0), colspan=3))\n",
"ax2 = fig.add_subplot(gs[-1],sharex=ax1)\n",
"\n",
"sca(ax1)\n",
"plt.plot(tfold_lc[isort_lc], model_lc[isort_lc] * 1e6, \"C1\", label=\"long cadence\",marker=',')\n",
"ylabel('$\\Delta F / F - 1$ (ppm)')\n",
"plot(tfold_lc[isort_lc],map_soln[\"light_curve\"][isort_lc] * 1e6, label=\"long cadence b =0 \",marker=',')\n",
"plt.setp(ax1.get_xticklabels(), visible=False)\n",
"\n",
"sca(ax2)\n",
"difference = map_soln[\"light_curve\"][isort_lc]-model_lc[isort_lc]\n",
"\n",
"plot(tfold_lc[isort_lc],difference * 1e6, label=\"long cadence b =0 \",marker=',')\n",
"ylabel('Difference (ppm)')\n",
"xlabel('Time Since Mid-Transit (days)')\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## this is the line that takes for ever"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'figure' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-42065370a04e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Plot long cadence light curve\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mpm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mxo_model_sc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUniform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"r\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlower\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.00\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupper\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtestval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.05\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'figure' is not defined"
]
}
],
"source": [
"# Plot long cadence light curve\n",
"figure()\n",
"\n",
"with pm.Model() as xo_model_sc:\n",
" r = pm.Uniform(\"r\", lower=0.00, upper=0.1, testval=0.05)\n",
" b = 0 \n",
" logrho = pm.Uniform(\"logrho\", lower=-10, upper=10, testval=0)\n",
" rho = pm.Deterministic(\"rho\", 10**logrho)\n",
" orbit = xo.orbits.KeplerianOrbit(period=truth['per'], t0=truth['tc'], b=b, rho_star=rho)\n",
" light_curves = xo.LimbDarkLightCurve(u).get_light_curve(orbit=orbit, r=r, t=t_sc, texp=None)\n",
" light_curve = pm.math.sum(light_curves, axis=-1) \n",
" #pm.Deterministic(\"light_curve\", light_curve)\n",
" pm.Normal(\"obs\", mu=light_curve, sd=1, observed=model_sc)\n",
" #map_soln = xo.optimize(start=xo_model_sc.test_point,vars=[r])\n",
" #map_soln = xo.optimize(start=map_soln)\n",
"\n",
"print(map_soln)\n",
" \n",
"fig = figure(figsize=(6,6))\n",
"gs = GridSpec(3, 1, figure=fig)\n",
"ax1 = fig.add_subplot(gs[0:-1])\n",
"# identical to ax1 = plt.subplot(gs.new_subplotspec((0, 0), colspan=3))\n",
"ax2 = fig.add_subplot(gs[-1],sharex=ax1)\n",
"\n",
"sca(ax1)\n",
"plt.plot(tfold_sc[isort_sc], model_sc[isort_sc] * 1e6, \"C1\", label=\"long cadence\",marker=',')\n",
"ylabel('$\\Delta F / F - 1$ (ppm)')\n",
"plot(tfold_sc[isort_sc],map_soln[\"light_curve\"][isort_sc] * 1e6, label=\"long cadence b =0 \",marker=',')\n",
"plt.setp(ax1.get_xticklabels(), visible=False)\n",
"\n",
"sca(ax2)\n",
"difference = map_soln[\"light_curve\"][isort_sc]-model_sc[isort_sc]\n",
"\n",
"plot(tfold_sc[isort_sc],difference * 1e6, label=\"long cadence b =0 \",marker=',')\n",
"ylabel('Difference (ppm)')\n",
"xlabel('Time Since Mid-Transit (days)')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"len(t_lc)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot(t_sc,model_sc,',')\n",
"xlim(0,10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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